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Automating Customer Support: A Case Study on Ada's Integration with Square
Applicable Industries
- Apparel
- Telecommunications
Use Cases
- Demand Planning & Forecasting
- Time Sensitive Networking
The Challenge
Square, a digital payment company, was facing challenges in providing efficient and personalized customer support. The company needed a solution that could act as the first point of contact for Global Customer Success, providing personalized self-service, triaging seller questions and product issues, and routing the seller to the ideal contact channel when necessary. The goal was to reduce customer effort and demand for support, while increasing the efficiency and impact of Customer Success. The company also wanted to optimize support channels by leveraging seller profile data to display optimal support paths and reduce customer frustration. Furthermore, Square aimed to reduce seller effort and increase advocate efficiency by providing greater context to enhance advocate efficiency when handling common issues.
About The Customer
The customer in this case study is Square, a digital payment company that provides a range of business services, payments, and point-of-sale (POS) solutions. Square's services include payments, hardware, and software that enable businesses to accept payments, track sales, and manage operations. Square's customer base is diverse, ranging from small businesses to large enterprises. The company operates globally, supporting multiple markets and languages. Square was seeking a solution to improve the efficiency and personalization of its customer support, reduce customer effort and demand for support, and increase the efficiency and impact of its Customer Success team.
The Solution
Square integrated Ada, an AI-powered customer service platform, to automate their customer support. Ada was designed to provide personalized self-service, triage seller questions and product issues, and route the seller to the ideal contact channel when necessary. By using seller profile and customer journey data, Ada could identify a seller’s intent and narrow down the list of potential solutions. Ada was also capable of detecting customer specific information such as device type, browser, or OS to streamline technical troubleshooting. Furthermore, Ada was used to optimize support channels by leveraging seller profile data to display optimal support paths. Ada also reduced seller effort and increased advocate efficiency by passing the chat transcript as an advocate reference point for context when a case was created. Ada was designed to be flexible and scalable for a diversity of markets and use cases, supporting 100+ languages and capable of handling 10 million+ conversations daily.
Operational Impact
Quantitative Benefit
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